A Practical Framework for Auditing Bias in Recommendation Algorithms

This article introduces a four-step methodology—scope, identify, implement, and flag—for auditing attribute association bias in latent factor recommendation (LFR) algorithms. Built upon the SIIM framework, it helps practitioners determine what to analyze, apply appropriate evaluation methods, and statistically test for significance in detected bias. The framework integrates both qualitative (e.g., PCA visualization) and quantitative (e.g., WEAT, R-RIPA, classification) tools to measure bias strength and direction. By operationalizing bias detection in a practical, step-by-step manner, it provides researchers and engineers with a replicable process for identifying and validating fairness issues in real-world AI recommendation systems.